Functional interaction of CCAAT/enhancer-binding-protein-α basic region mutants with E2F transcription factors and DNA

The transcription factor CCAAT/enhancer-binding protein {alpha} (C/EBP{alpha}) regulates cell cycle arrest and terminal differentiation of neutrophils and adipocytes. Mutations in the basic leucine zipper domain (bZip) of C/EBP{alpha} are associated with acute myeloid leukemia. A widely used murine transforming C/EBP{alpha} basic region mutant (BRM2) entails two bZip point mutations (I294A/R297A). BRM2 has been discordantly described as defective for DNA binding or defective for interaction with E2F. We have separated the two BRM2 mutations to shed light on the intertwined reciprocity between C/EBP{alpha}-E2F-DNA interactions. Both, C/EBP{alpha} I294A and R297A retain transactivation capacity and interaction with E2F-DP. The C/EBP{alpha} R297A mutation destabilized DNA binding, whereas the C/EBP{alpha} I294A mutation enhanced binding to DNA. The C/EBP{alpha} R297A mutant, like BRM2, displayed enhanced interaction with E2F-DP but failed to repress E2F-dependent transactivation although both mutants were readily suppressed by E2F1 for transcription through C/EBP cis-regulatory sites. In contrast, the DNA binding enhanced C/EBP{alpha} I294A mutant displayed increased repression of E2F-DP mediated transactivation and resisted E2F-DP mediated repression. Thus, the efficient repression of E2F dependent S-phase genes and the activation of differentiation genes reside in the balanced DNA binding capacity of C/EBP{alpha}

The solution space of metabolic networks: producibility, robustness and fluctuations

Flux analysis is a class of constraint-based approaches to the study of biochemical reaction networks: they are based on determining the reaction flux configurations compatible with given stoichiometric and thermodynamic constraints. One of its main areas of application is the study of cellular metabolic networks. We briefly and selectively review the main approaches to this problem and then, building on recent work, we provide a characterization of the productive capabilities of the metabolic network of the bacterium E.coli in a specified growth medium in terms of the producible biochemical species. While a robust and physiologically meaningful production profile clearly emerges (including biomass components, biomass products, waste etc.), the underlying constraints still allow for significant fluctuations even in key metabolites like ATP and, as a consequence, apparently lay the ground for very different growth scenarios.Comment: 10 pages, prepared for the Proceedings of the International Workshop on Statistical-Mechanical Informatics, March 7-10, 2010, Kyoto, Japa

Diffusion algebras

We define the notion of "diffusion algebras". They are quadratic Poincare-Birkhoff-Witt (PBW) algebras which are useful in order to find exact expressions for the probability distributions of stationary states appearing in one-dimensional stochastic processes with exclusion. One considers processes in which one has N species, the number of particles of each species being conserved. All diffusion algebras are obtained. The known examples already used in applications are special cases in our classification. To help the reader interested in physical problems, the cases N=3 and 4 are listed separately.Comment: 29 pages; minor misprints corrected, few references adde

Mapping the genetic architecture of gene expression in human liver

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. © 2008 Schadt et al

Small multi-purpose reservoir ensemble planning

People living in arid areas with highly variable rainfall, experience droughts and floods and often have insecure livelihoods. Small multi-purpose reservoirs are a widely used form of infrastructure for the provision of water. They supply water for domestic use, livestock watering, small scale irrigation, and other beneficial uses. The reservoirs are hydrologically linked by the streams that have been dammed. Although reservoirs store a large quantity of water and have a significant effect on downstream flows, they have rarely been considered as systems, with synergies and tradeoffs resulting from the number and density of their structures. Often reservoirs were constructed in a series of projects funded by different agencies, at different times, with little or no coordination among the implementing partners. A significant number are functioning sub-optimally and/or are falling into disrepair. This indicates that there is room for improvement in the planning, operation, and maintenance of small reservoirs. The water management institutions in Volta, Limpopo, and Sao Francisco Basins are being revamped to better serve their constituencies. We have an opportunity to collaborate with government officials, stakeholders, and farmers who are actively looking for ways to improve the planning process. The Small Reservoir Project team developed a tool kit to support the planning, development, and management of small reservoir ensembles on the basin level and the use of small multi-purpose reservoirs that are properly located, well designed, operated and maintained in sustainable fashion, and economically viable on the local/community level. There are tools to improve intervention planning, storage estimation and the analysis of the hydrology, ecology and health of small reservoirs. There ara also tools for the analysis of institutional and economic aspects of the reservoirs. The toolkit not only includes the necessary analytical instruments, but also a set of process oriented tools for improved participatory decision making. The Tool Kit is meant to be a living “document” with additional tools and experiences to be added as they are developed

Explainable AI using expressive Boolean formulas

We propose and implement an interpretable machine learning classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit scoring and diagnosis of medical conditions. The Boolean formula defines a rule with tunable complexity (or interpretability), according to which input data are classified. Such a formula can include any operator that can be applied to one or more Boolean variables, thus providing higher expressivity compared to more rigid rule-based and tree-based approaches. The classifier is trained using native local optimization techniques, efficiently searching the space of feasible formulas. Shallow rules can be determined by fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers, potentially powered by special purpose hardware or quantum devices. We combine the expressivity and efficiency of the native local optimizer with the fast operation of these devices by executing non-local moves that optimize over subtrees of the full Boolean formula. We provide extensive numerical benchmarking results featuring several baselines on well-known public datasets. Based on the results, we find that the native local rule classifier is generally competitive with the other classifiers. The addition of non-local moves achieves similar results with fewer iterations, and therefore using specialized or quantum hardware could lead to a speedup by fast proposal of non-local moves.Comment: 28 pages, 16 figures, 4 table

Procalcitonin for diagnosis of infection and guide to antibiotic decisions: past, present and future

There are a number of limitations to using conventional diagnostic markers for patients with clinical suspicion of infection. As a consequence, unnecessary and prolonged exposure to antimicrobial agents adversely affect patient outcomes, while inappropriate antibiotic therapy increases antibiotic resistance. A growing body of evidence supports the use of procalcitonin (PCT) to improve diagnosis of bacterial infections and to guide antibiotic therapy. For patients with upper and lower respiratory tract infection, post-operative infections and for severe sepsis patients in the intensive care unit, randomized-controlled trials have shown a benefit of using PCT algorithms to guide decisions about initiation and/or discontinuation of antibiotic therapy. For some other types of infections, observational studies have shown promising first results, but further intervention studies are needed before use of PCT in clinical routine can be recommended. The aim of this review is to summarize the current evidence for PCT in different infections and clinical settings, and discuss the reliability of this marker when used with validated diagnostic algorithms